Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations8000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows3797
Duplicate rows (%)< 0.1%
Total size in memory1.2 GiB
Average record size in memory155.0 B

Variable types

Numeric11
Categorical11

Alerts

Dataset has 3797 (< 0.1%) duplicate rowsDuplicates
Crossing is highly overall correlated with Traffic_SignalHigh correlation
Traffic_Signal is highly overall correlated with CrossingHigh correlation
Amenity is highly imbalanced (95.1%) Imbalance
Crossing is highly imbalanced (57.1%) Imbalance
Give_Way is highly imbalanced (97.6%) Imbalance
Junction is highly imbalanced (73.7%) Imbalance
No_Exit is highly imbalanced (98.6%) Imbalance
Railway is highly imbalanced (96.0%) Imbalance
Stop is highly imbalanced (90.6%) Imbalance
Traffic_Calming is highly imbalanced (99.4%) Imbalance
Duration_Seconds is highly skewed (γ1 = 39.95090881) Skewed
Severity is uniformly distributed Uniform
Distance(mi) has 3841953 (48.0%) zeros Zeros
Wind_Direction has 692698 (8.7%) zeros Zeros
Wind_Speed(mph) has 527049 (6.6%) zeros Zeros

Reproduction

Analysis started2024-11-05 18:05:57.533407
Analysis finished2024-11-05 18:11:36.803186
Duration5 minutes and 39.27 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distance(mi)
Real number (ℝ)

Zeros 

Distinct2467272
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64652694
Minimum0
Maximum441.75
Zeros3841953
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:36.903240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0076551313
Q30.492
95-th percentile3.133
Maximum441.75
Range441.75
Interquartile range (IQR)0.492

Descriptive statistics

Standard deviation2.0587579
Coefficient of variation (CV)3.1843343
Kurtosis844.9908
Mean0.64652694
Median Absolute Deviation (MAD)0.0076551313
Skewness15.100567
Sum5172215.5
Variance4.2384842
MonotonicityNot monotonic
2024-11-05T19:11:36.973423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3841953
48.0%
0.01 129240
 
1.6%
0.009999999776 6569
 
0.1%
0.009 5309
 
0.1%
0.008 5090
 
0.1%
0.007 4267
 
0.1%
0.011 4025
 
0.1%
0.012 4012
 
0.1%
0.02 3970
 
< 0.1%
0.03 3923
 
< 0.1%
Other values (2467262) 3991642
49.9%
ValueCountFrequency (%)
0 3841953
48.0%
1.366643449 × 10-81
 
< 0.1%
3.888559948 × 10-81
 
< 0.1%
7.182865409 × 10-81
 
< 0.1%
1.500800951 × 10-71
 
< 0.1%
1.548788507 × 10-71
 
< 0.1%
2.043594519 × 10-71
 
< 0.1%
2.410619072 × 10-71
 
< 0.1%
2.416775028 × 10-71
 
< 0.1%
3.606069733 × 10-71
 
< 0.1%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
323.5073867 1
< 0.1%
254.3999939 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%
193.4799957 1
< 0.1%
186.4319361 1
< 0.1%
183.1199951 1
< 0.1%
162.69119 1
< 0.1%

Street
Real number (ℝ)

Distinct318549
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193884.17
Minimum1
Maximum320206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.5 MiB
2024-11-05T19:11:37.092849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38793.95
Q1155796
median215885
Q3233728
95-th percentile302158
Maximum320206
Range320205
Interquartile range (IQR)77932

Descriptive statistics

Standard deviation75159.954
Coefficient of variation (CV)0.3876539
Kurtosis-0.10154067
Mean193884.17
Median Absolute Deviation (MAD)42238
Skewness-0.6628632
Sum1.5510733 × 1012
Variance5.6490186 × 109
MonotonicityNot monotonic
2024-11-05T19:11:37.161310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216455 100106
 
1.3%
216115 65500
 
0.8%
216456 61432
 
0.8%
215646 42411
 
0.5%
215652 37861
 
0.5%
216351 34248
 
0.4%
216348 34084
 
0.4%
216006 32917
 
0.4%
216302 32168
 
0.4%
216116 30692
 
0.4%
Other values (318539) 7528581
94.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 13
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 5
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 9
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
320206 3
 
< 0.1%
320205 1
 
< 0.1%
320198 1
 
< 0.1%
320197 11
 
< 0.1%
320196 28
< 0.1%
320195 2
 
< 0.1%
320193 2
 
< 0.1%
320192 1
 
< 0.1%
320189 6
 
< 0.1%
320188 3
 
< 0.1%

Zipcode
Real number (ℝ)

Distinct734841
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402002.17
Minimum0
Maximum774619
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size30.5 MiB
2024-11-05T19:11:37.268481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33170
Q1195535
median421150
Q3605486
95-th percentile735447
Maximum774619
Range774619
Interquartile range (IQR)409951

Descriptive statistics

Standard deviation224109.79
Coefficient of variation (CV)0.55748402
Kurtosis-1.188489
Mean402002.17
Median Absolute Deviation (MAD)194543
Skewness-0.14829214
Sum3.2160174 × 1012
Variance5.0225197 × 1010
MonotonicityNot monotonic
2024-11-05T19:11:37.336843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322363 7562
 
0.1%
650715 7443
 
0.1%
705282 7303
 
0.1%
336122 7162
 
0.1%
328573 7151
 
0.1%
333271 7095
 
0.1%
701490 7006
 
0.1%
332886 6841
 
0.1%
654009 6814
 
0.1%
281565 6767
 
0.1%
Other values (734831) 7928856
99.1%
ValueCountFrequency (%)
0 5
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
6 5
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
774619 1
 
< 0.1%
774618 12
 
< 0.1%
774617 1
 
< 0.1%
774615 1
 
< 0.1%
774613 2
 
< 0.1%
774612 2
 
< 0.1%
774611 2
 
< 0.1%
774610 1
 
< 0.1%
774609 38
< 0.1%
774608 4
 
< 0.1%

Temperature(F)
Real number (ℝ)

Distinct4809561
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.908629
Minimum-45
Maximum196
Zeros1182
Zeros (%)< 0.1%
Negative9370
Negative (%)0.1%
Memory size61.0 MiB
2024-11-05T19:11:37.466507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile31.027254
Q153
median66.522304
Q377
95-th percentile88
Maximum196
Range241
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.584628
Coefficient of variation (CV)0.27515264
Kurtosis0.20202515
Mean63.908629
Median Absolute Deviation (MAD)11.522304
Skewness-0.61884868
Sum5.1126903 × 108
Variance309.21915
MonotonicityNot monotonic
2024-11-05T19:11:37.544567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 77449
 
1.0%
73 77292
 
1.0%
72 71532
 
0.9%
75 70617
 
0.9%
68 69368
 
0.9%
70 68091
 
0.9%
79 66440
 
0.8%
63 63299
 
0.8%
64 62840
 
0.8%
66 61681
 
0.8%
Other values (4809551) 7311391
91.4%
ValueCountFrequency (%)
-45 1
 
< 0.1%
-38 1
 
< 0.1%
-35 3
 
< 0.1%
-30.20348744 1
 
< 0.1%
-30 1
 
< 0.1%
-29 5
 
< 0.1%
-28 4
 
< 0.1%
-27.9 4
 
< 0.1%
-27 6
< 0.1%
-26 13
< 0.1%
ValueCountFrequency (%)
196 2
 
< 0.1%
174 1
 
< 0.1%
172 1
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
140 7
< 0.1%
139.0434119 1
 
< 0.1%
138.287613 1
 
< 0.1%
137.4145748 1
 
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct4792103
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.500493
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:37.644964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q149.639085
median67.034298
Q382.22877
95-th percentile94.862615
Maximum100
Range99
Interquartile range (IQR)32.589685

Descriptive statistics

Standard deviation22.016765
Coefficient of variation (CV)0.34134258
Kurtosis-0.48012407
Mean64.500493
Median Absolute Deviation (MAD)16.034298
Skewness-0.50488945
Sum5.1600395 × 108
Variance484.73794
MonotonicityNot monotonic
2024-11-05T19:11:37.710958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 134415
 
1.7%
93 125944
 
1.6%
87 73772
 
0.9%
90 73470
 
0.9%
89 57696
 
0.7%
96 57101
 
0.7%
94 54720
 
0.7%
84 54267
 
0.7%
81 53811
 
0.7%
82 52521
 
0.7%
Other values (4792093) 7262283
90.8%
ValueCountFrequency (%)
1 23
 
< 0.1%
2 59
< 0.1%
2.024827811 1
 
< 0.1%
2.031671366 1
 
< 0.1%
2.046151378 1
 
< 0.1%
2.123670093 1
 
< 0.1%
2.239074071 1
 
< 0.1%
2.295667086 1
 
< 0.1%
2.309676245 1
 
< 0.1%
2.316322161 1
 
< 0.1%
ValueCountFrequency (%)
100 134415
1.7%
99.99995562 1
 
< 0.1%
99.99992903 1
 
< 0.1%
99.99984587 1
 
< 0.1%
99.99984208 1
 
< 0.1%
99.99983702 1
 
< 0.1%
99.99975781 1
 
< 0.1%
99.99972269 1
 
< 0.1%
99.99971379 1
 
< 0.1%
99.99947003 1
 
< 0.1%

Pressure(in)
Real number (ℝ)

Distinct4856560
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.432627
Minimum0
Maximum58.63
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:37.824777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.221655
Q129.252852
median29.786829
Q329.99
95-th percentile30.205877
Maximum58.63
Range58.63
Interquartile range (IQR)0.73714843

Descriptive statistics

Standard deviation1.0869118
Coefficient of variation (CV)0.036928808
Kurtosis16.028905
Mean29.432627
Median Absolute Deviation (MAD)0.28471238
Skewness-3.4562072
Sum2.3546102 × 108
Variance1.1813774
MonotonicityNot monotonic
2024-11-05T19:11:37.890814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 51728
 
0.6%
29.99 49996
 
0.6%
29.94 49447
 
0.6%
30.01 48844
 
0.6%
30.04 46215
 
0.6%
29.97 46190
 
0.6%
29.91 46063
 
0.6%
30 45235
 
0.6%
29.95 45071
 
0.6%
30.03 45013
 
0.6%
Other values (4856550) 7526198
94.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.02 1
 
< 0.1%
2.99 2
< 0.1%
3 2
< 0.1%
3.04 3
< 0.1%
6.461334967 1
 
< 0.1%
8.971897344 1
 
< 0.1%
9.9 1
 
< 0.1%
12.1014521 1
 
< 0.1%
16.82 1
 
< 0.1%
ValueCountFrequency (%)
58.63 1
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 3
< 0.1%
58.04 1
 
< 0.1%
57.74 1
 
< 0.1%
57.54 1
 
< 0.1%
56.54 1
 
< 0.1%
50.87824555 1
 
< 0.1%
47.35128167 1
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct1392939
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2039657
Minimum0
Maximum111
Zeros2980
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:37.969673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median10
Q310
95-th percentile10
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.34156
Coefficient of variation (CV)0.25440773
Kurtosis83.445472
Mean9.2039657
Median Absolute Deviation (MAD)0
Skewness2.3753125
Sum73631726
Variance5.4829034
MonotonicityNot monotonic
2024-11-05T19:11:38.045582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 5987921
74.8%
7 92736
 
1.2%
9 78800
 
1.0%
8 61352
 
0.8%
5 58509
 
0.7%
6 51874
 
0.6%
2 51421
 
0.6%
3 49244
 
0.6%
4 49047
 
0.6%
1 42569
 
0.5%
Other values (1392929) 1476527
 
18.5%
ValueCountFrequency (%)
0 2980
< 0.1%
0.002356447974 1
 
< 0.1%
0.002360799027 1
 
< 0.1%
0.002670320859 1
 
< 0.1%
0.003181793946 1
 
< 0.1%
0.005377703439 1
 
< 0.1%
0.005413849868 1
 
< 0.1%
0.005430134363 1
 
< 0.1%
0.005564015582 1
 
< 0.1%
0.006235744162 1
 
< 0.1%
ValueCountFrequency (%)
111 3
 
< 0.1%
100 22
 
< 0.1%
91.15713689 1
 
< 0.1%
90 6
 
< 0.1%
80 117
< 0.1%
79.66546803 1
 
< 0.1%
78.57553526 1
 
< 0.1%
77.91303542 1
 
< 0.1%
76.86772822 1
 
< 0.1%
76.45712066 1
 
< 0.1%

Wind_Direction
Real number (ℝ)

Zeros 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.125948
Minimum0
Maximum22
Zeros692698
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2024-11-05T19:11:38.122967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum22
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2996058
Coefficient of variation (CV)0.62212507
Kurtosis-1.0543052
Mean10.125948
Median Absolute Deviation (MAD)5
Skewness-0.0017965965
Sum81007580
Variance39.685034
MonotonicityNot monotonic
2024-11-05T19:11:38.170501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 692698
 
8.7%
11 486018
 
6.1%
14 458177
 
5.7%
9 431464
 
5.4%
13 430611
 
5.4%
8 418096
 
5.2%
12 414071
 
5.2%
15 399782
 
5.0%
19 355484
 
4.4%
7 350071
 
4.4%
Other values (13) 3563528
44.5%
ValueCountFrequency (%)
0 692698
8.7%
1 289584
3.6%
2 294099
3.7%
3 306954
3.8%
4 238578
 
3.0%
5 346381
4.3%
6 342316
4.3%
7 350071
4.4%
8 418096
5.2%
9 431464
5.4%
ValueCountFrequency (%)
22 96704
 
1.2%
21 224789
2.8%
20 310973
3.9%
19 355484
4.4%
18 224925
2.8%
17 285990
3.6%
16 285273
3.6%
15 399782
5.0%
14 458177
5.7%
13 430611
5.4%

Wind_Speed(mph)
Real number (ℝ)

Zeros 

Distinct4476850
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6686545
Minimum0
Maximum1087
Zeros527049
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:38.270053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6986642
median7
Q310.133447
95-th percentile16
Maximum1087
Range1087
Interquartile range (IQR)5.4347824

Descriptive statistics

Standard deviation4.8336763
Coefficient of variation (CV)0.6303161
Kurtosis1064.0514
Mean7.6686545
Median Absolute Deviation (MAD)2.74549
Skewness7.0866823
Sum61349236
Variance23.364427
MonotonicityNot monotonic
2024-11-05T19:11:38.329086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 527049
 
6.6%
5 258795
 
3.2%
6 248080
 
3.1%
3 245811
 
3.1%
7 228292
 
2.9%
8 202102
 
2.5%
9 179375
 
2.2%
10 145764
 
1.8%
12 124495
 
1.6%
4.6 119988
 
1.5%
Other values (4476840) 5720249
71.5%
ValueCountFrequency (%)
0 527049
6.6%
3.706551085 × 10-61
 
< 0.1%
1.202472706 × 10-51
 
< 0.1%
2.554059301 × 10-51
 
< 0.1%
2.590362688 × 10-51
 
< 0.1%
2.608667219 × 10-51
 
< 0.1%
2.838631237 × 10-51
 
< 0.1%
3.73053303 × 10-51
 
< 0.1%
4.204411388 × 10-51
 
< 0.1%
4.307930038 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1087 1
 
< 0.1%
822.8 5
< 0.1%
812 1
 
< 0.1%
703.1 1
 
< 0.1%
666.192746 1
 
< 0.1%
616.2664909 1
 
< 0.1%
443.0823779 1
 
< 0.1%
328 1
 
< 0.1%
322.6520728 1
 
< 0.1%
255 1
 
< 0.1%

Weather_Condition
Real number (ℝ)

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.781371
Minimum0
Maximum139
Zeros81
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2024-11-05T19:11:38.407859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q115
median21
Q383
95-th percentile89
Maximum139
Range139
Interquartile range (IQR)68

Descriptive statistics

Standard deviation33.579085
Coefficient of variation (CV)0.80368557
Kurtosis-1.371864
Mean41.781371
Median Absolute Deviation (MAD)15
Skewness0.47934693
Sum3.3425097 × 108
Variance1127.5549
MonotonicityNot monotonic
2024-11-05T19:11:38.478599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2081241
26.0%
83 695827
 
8.7%
6 558047
 
7.0%
7 522056
 
6.5%
89 386452
 
4.8%
86 289292
 
3.6%
60 211374
 
2.6%
103 112163
 
1.4%
68 92621
 
1.2%
8 91431
 
1.1%
Other values (130) 2959496
37.0%
ValueCountFrequency (%)
0 81
 
< 0.1%
1 142
 
< 0.1%
2 43
 
< 0.1%
3 496
 
< 0.1%
4 562
 
< 0.1%
5 217
 
< 0.1%
6 558047
7.0%
7 522056
6.5%
8 91431
 
1.1%
9 84164
 
1.1%
ValueCountFrequency (%)
139 126
 
< 0.1%
138 4715
0.1%
137 130
 
< 0.1%
136 206
 
< 0.1%
135 153
 
< 0.1%
134 141
 
< 0.1%
133 1202
 
< 0.1%
132 2382
 
< 0.1%
131 7559
0.1%
130 604
 
< 0.1%

Amenity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7955955 
1
 
44045

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Length

2024-11-05T19:11:38.561084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:38.623668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7955955
99.4%
1 44045
 
0.6%

Crossing
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7297963 
1
 
702037

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Length

2024-11-05T19:11:38.682446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:38.722009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7297963
91.2%
1 702037
 
8.8%

Give_Way
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7980940 
1
 
19060

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Length

2024-11-05T19:11:38.788900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:38.827750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7980940
99.8%
1 19060
 
0.2%

Junction
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7643742 
1
 
356258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

Length

2024-11-05T19:11:38.891638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.206949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7643742
95.5%
1 356258
 
4.5%

No_Exit
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7989643 
1
 
10357

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Length

2024-11-05T19:11:39.260338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.312545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7989643
99.9%
1 10357
 
0.1%

Railway
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7965143 
1
 
34857

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Length

2024-11-05T19:11:39.353427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.400349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7965143
99.6%
1 34857
 
0.4%

Stop
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7904107 
1
 
95893

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Length

2024-11-05T19:11:39.462908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.515009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7904107
98.8%
1 95893
 
1.2%

Traffic_Calming
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7996478 
1
 
3522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Length

2024-11-05T19:11:39.565556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.617868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7996478
> 99.9%
1 3522
 
< 0.1%

Traffic_Signal
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
0
7006481 
1
993519 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Length

2024-11-05T19:11:39.672604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.720946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Most occurring characters

ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7006481
87.6%
1 993519
 
12.4%

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
1
5285571 
0
2714429 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Length

2024-11-05T19:11:39.765419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:39.815584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Most occurring characters

ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5285571
66.1%
0 2714429
33.9%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct4015276
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33249.946
Minimum120
Maximum1.3417683 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 MiB
2024-11-05T19:11:39.902766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile1641.7062
Q11789.4793
median3524.1711
Q37114.4937
95-th percentile21600
Maximum1.3417683 × 108
Range1.3417671 × 108
Interquartile range (IQR)5325.0144

Descriptive statistics

Standard deviation899417.34
Coefficient of variation (CV)27.05019
Kurtosis1878.7975
Mean33249.946
Median Absolute Deviation (MAD)1745.1711
Skewness39.950909
Sum2.6599957 × 1011
Variance8.0895156 × 1011
MonotonicityNot monotonic
2024-11-05T19:11:39.977274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 563635
 
7.0%
900 195152
 
2.4%
2700 169903
 
2.1%
1800 99367
 
1.2%
3600 70937
 
0.9%
2098 41993
 
0.5%
3898 39340
 
0.5%
4500 37442
 
0.5%
7200 27052
 
0.3%
1785 22535
 
0.3%
Other values (4015266) 6732644
84.2%
ValueCountFrequency (%)
120 1
 
< 0.1%
150 1
 
< 0.1%
180 5
 
< 0.1%
210 2
 
< 0.1%
240 1
 
< 0.1%
270 2
 
< 0.1%
300 7
< 0.1%
330 17
< 0.1%
337 1
 
< 0.1%
340 1
 
< 0.1%
ValueCountFrequency (%)
134176830 2
< 0.1%
106135755 1
< 0.1%
94697995 1
< 0.1%
94697974 1
< 0.1%
94697965 1
< 0.1%
94611586 1
< 0.1%
94611566 1
< 0.1%
94611563 1
< 0.1%
94611549 1
< 0.1%
94611545 1
< 0.1%

Severity
Categorical

Uniform 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.0 MiB
2
2000000 
1
2000000 
3
2000000 
4
2000000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Length

2024-11-05T19:11:40.049213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T19:11:40.090147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Most occurring characters

ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2000000
25.0%
1 2000000
25.0%
3 2000000
25.0%
4 2000000
25.0%

Interactions

2024-11-05T19:11:04.846726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:32.160073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:41.659623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:50.894320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:00.192844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:09.733003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:19.218855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:28.466982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:37.773889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:46.779493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:55.792295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:05.634273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:32.994186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:42.468568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:51.736647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:01.035175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:10.571678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:20.008779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:29.306525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:38.581302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:47.579291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:56.616437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:06.442579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:33.801780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:43.332560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:52.543094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:01.875277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:11.410799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:20.805501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:30.127772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:39.390475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:48.365119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:57.435217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:07.285365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:34.920901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:44.192153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:53.387402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:02.692927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:12.315540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:21.655013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:31.023181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:40.216033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:49.221403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:58.271500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:08.149034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:35.793046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:45.040460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:54.246089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:03.592489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:13.135009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:22.497357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:31.916392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:41.034792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:50.076014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:59.094113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:08.997396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:36.648023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:45.894985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:55.093841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:04.492856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:14.028451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:23.269992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:32.769085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:41.868750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:50.924286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:59.913032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:09.848488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:37.502257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:46.743831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:55.948341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:05.395721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:14.922443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:24.133070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:33.550246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:42.706943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:51.760993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:00.752815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:10.621388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:38.289236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:47.557683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:56.788934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:06.213814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:15.758830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:25.034458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:34.374734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:43.476808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:52.537324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:01.563686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:11.475818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:39.144648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:48.398111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:57.631351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:07.106337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:16.655670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:25.896923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:35.271805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:44.294950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:53.334991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:02.390205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:12.257849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:39.926197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:49.206071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:58.457734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:07.929178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:17.487012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:26.696385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:36.086622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:45.106793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:54.110379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:03.173404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:13.014642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:40.804847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:50.046700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:09:59.293938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:08.830142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:18.364290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:27.557551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:36.961976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:45.912408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:10:54.962408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T19:11:03.997490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T19:11:40.182794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopStreetTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Weather_ConditionWind_DirectionWind_Speed(mph)Zipcode
Amenity1.0000.0150.1270.0000.0010.0130.0130.0120.0220.0160.0570.0700.0350.0500.0060.0420.0890.0040.0160.0110.0000.033
Civil_Twilight0.0151.0000.0740.0050.0160.0110.2150.0010.0100.0210.0100.2700.0140.0690.2610.0020.0950.0120.0880.0950.0020.058
Crossing0.1270.0741.0000.0040.0080.0590.2170.0570.0610.0850.1420.2310.0840.1930.1320.0330.5350.0080.0670.0190.0000.150
Distance(mi)0.0000.0050.0041.0000.3750.0000.0430.0010.0000.0400.0000.0080.001-0.151-0.2190.0010.005-0.0650.002-0.0140.024-0.117
Duration_Seconds0.0010.0160.0080.3751.0000.0010.0080.0070.000-0.0520.0010.0210.004-0.152-0.0990.0000.010-0.016-0.028-0.031-0.030-0.099
Give_Way0.0130.0110.0590.0000.0011.0000.0100.0030.0080.0050.0030.0300.0340.0310.0070.0030.0590.0050.0120.0050.0000.032
Humidity(%)0.0130.2150.2170.0430.0080.0101.0000.0180.0150.1170.0080.1000.0170.072-0.3200.0050.211-0.4390.103-0.201-0.233-0.167
Junction0.0120.0010.0570.0010.0070.0030.0181.0000.0010.0510.0020.1020.0150.1270.0340.0010.0720.0040.0470.0340.0000.074
No_Exit0.0220.0100.0610.0000.0000.0080.0150.0011.0000.0120.0050.0260.0260.0200.0090.0290.0350.0090.0090.0050.0000.016
Pressure(in)0.0160.0210.0850.040-0.0520.0050.1170.0510.0121.0000.0110.1110.0020.052-0.0730.0050.0990.0340.0640.0030.007-0.102
Railway0.0570.0100.1420.0000.0010.0030.0080.0020.0050.0111.0000.0450.0130.0190.0050.0040.0510.0040.0140.0170.0000.035
Severity0.0700.2700.2310.0080.0210.0300.1000.1020.0260.1110.0451.0000.1070.2590.1730.0200.2710.0190.1940.1050.0010.162
Stop0.0350.0140.0840.0010.0040.0340.0170.0150.0260.0020.0130.1071.0000.0640.0100.0230.0260.0050.0280.0180.0000.049
Street0.0500.0690.193-0.151-0.1520.0310.0720.1270.0200.0520.0190.2590.0641.0000.0360.0140.201-0.0250.0400.0170.022-0.012
Temperature(F)0.0060.2610.132-0.219-0.0990.007-0.3200.0340.009-0.0730.0050.1730.0100.0361.0000.0020.1420.2270.0490.0790.0480.052
Traffic_Calming0.0420.0020.0330.0010.0000.0030.0050.0010.0290.0050.0040.0200.0230.0140.0021.0000.0080.0050.0060.0050.0000.013
Traffic_Signal0.0890.0950.5350.0050.0100.0590.2110.0720.0350.0990.0510.2710.0260.2010.1420.0081.0000.0040.0600.0210.0000.171
Visibility(mi)0.0040.0120.008-0.065-0.0160.005-0.4390.0040.0090.0340.0040.0190.005-0.0250.2270.0050.0041.000-0.1200.0880.0450.030
Weather_Condition0.0160.0880.0670.002-0.0280.0120.1030.0470.0090.0640.0140.1940.0280.0400.0490.0060.060-0.1201.0000.0440.133-0.086
Wind_Direction0.0110.0950.019-0.014-0.0310.005-0.2010.0340.0050.0030.0170.1050.0180.0170.0790.0050.0210.0880.0441.0000.3460.053
Wind_Speed(mph)0.0000.0020.0000.024-0.0300.000-0.2330.0000.0000.0070.0000.0010.0000.0220.0480.0000.0000.0450.1330.3461.0000.002
Zipcode0.0330.0580.150-0.117-0.0990.032-0.1670.0740.016-0.1020.0350.1620.049-0.0120.0520.0130.1710.030-0.0860.0530.0021.000

Missing values

2024-11-05T19:11:13.363493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T19:11:17.855670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Distance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsSeverity
00.00021619064413375.057.029.6110.01110.01500000000014594.02
10.00024262118433358.053.029.4010.000.01501000100002686.02
20.6937087813563537.052.028.9310.01914.07000000001119440.02
30.61026319365528066.268.029.875.02211.524000100000121600.02
40.00016039218939868.047.030.1210.075.88300000000011787.02
50.28218246943835060.196.030.1810.0613.886000000000121600.02
60.0061568712327657.020.029.7210.0157.01500000000114516.02
70.42330149113237569.187.030.0310.0126.986000000000121600.02
81.28819975234084386.067.030.1310.01210.08300000000011343.02
90.00025473424897960.031.028.7710.000.01500000000113594.02
Distance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsSeverity
79999902.87623430196640237061.00000084.64969429.49949110.000000118.6751534000000000004876.2949864
79999911.5463301660041856144.65452889.67111529.9515615.843851218.84085863000000000021600.0000004
79999920.630240509586881762.58132277.08184928.7405778.889296613.2675776000000000014982.8048404
79999930.2619401276851934138.82122072.45017029.2615248.44372975.3112542900000000007564.8240224
79999941.08602110719049465577.03925955.76444828.38138510.0000001012.88222415000000000189113.0196294
79999950.34816419186762350349.70317747.11416327.33580410.00000095.96803416000000000020661.9192404
79999964.46552821633237328677.23330168.46487529.6543415.979788105.6337818100000000012030.8508684
79999970.68755430289415013254.97378966.33178430.09131110.000000143.0000005300000000018040.6894454
79999984.36140921627941385777.49100977.11934529.93984210.000000196.15427879000000000121600.0000004
79999990.5018462061243822366.76932569.85480630.16873510.00000074.66463588000000001121600.0000004

Duplicate rows

Most frequently occurring

Distance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_SecondsSeverity# duplicates
580.0013805761935977.027.027.1010.01520.01501000010102700.0128
600.0015165637720566.056.029.4810.036.01500000000013600.0127
260.008239062116965.031.027.2810.059.01501000000102700.0125
830.0019567274193370.023.029.7510.085.0150000000010900.0125
850.0019976373860453.061.030.0410.0113.0150000000000900.0125
1780.0021640871260863.045.030.0210.0177.08900000000012098.0124
1620.0021624070480340.083.030.0410.0149.0150000000000900.0122
1000.0020393114498065.093.029.469.01116.0600100000010900.0121
3610.0213576471454751.086.029.8910.0138.01500000000002098.0121
16840.3217033112359938.094.030.1210.000.0890000000000900.0121